How To Use Upselling and Cross-Selling to Increase eCommerce Sales

Increase Sales And Order Value With Smart Cross-Selling And Upselling

Meet the chatbot, your ultimate matchmaker.

Utilize upselling and cross-selling or die! eCommerce is not for the faint hearted. Stores are now leveraging chatbots to get that edge – read how.

E-commerce is competitive – very competitive.

This much everyone who has dipped their toes in the e-commerce shark infested waters knows.

If you are lucky enough to be able to get a potential customer’s attention, you need to leverage that opportunity to the greatest extent possible.

This is where upselling, cross-selling, and product recommendations come in.

Upselling is a sales technique where a seller induces the customer to purchase more expensive items, upgrades or other add-ons in an attempt to make a more profitable sale.

For example, when selling a tie you can highlight your diamond clad gold tie clips.

Cross-selling is the action or practice of selling an additional product or service to an existing customer.

For example, when selling a plumbing repair kit you might want to highlight your wrench offerings.

Many different companies have emerged to facilitate product recommendations for upselling and cross-selling in the e-commerce space.

Most of these offerings involve presenting a carousel of products on your e-commerce store when your visitor navigates to one of your product pages.

Customers Also Viewed

Other offerings include the same type of product recommendation but in an email to your store visitors typically sent after a visitor abandons a cart or leaves your website.

Cart Abandonment Email

Some of these recommendation engine offerings require that you manually select the products you want to upsell or cross sell for each one of your products.

Other companies boast algorithms that track your customers and do at least some of the recommendation curation work for your, i.e., they choose the products that you might want to upsell or cross-sell for you, and make recommendations to your customers.

These types of offers have become somewhat commonplace and largely mimick Amazon’s “Other customers also bought this” cross-sell approach.

While there are a number of different options for product recommendations engines, they fall into 2 main categories: unpersonalized and personalized upselling and cross-selling.

Unpersonalized product recommendation engines are perfect for those of you who haven’t collected enough data on your customers.

Because of this, they’re generally well suited to email blasts or campaigns that don’t require too much segmentation or personalization.

However, even if you do have data on your customers’ engagement and purchase history, you can still send out these unpersonalized campaigns to your list.

Examples of non-personalized recommendations include popular or new products or “you may also like” products.

You May Also Like

Personalized product recommendations leverage information you have about your customers to help you curate products that may be of particular interest to your customers above and beyond the generic here’s what’s new or popular recommendation.

For example, your product recommendation engine could highlight “recently viewed” products.

This product recommendation engine doesn’t actually require any in-depth data on your customers, because it’s based on items that someone engaged with when they were browsing your site.

The example below is from Amazon’s site while shopping for sunglasses.

Customers Who Viewed This Item Also Viewed

Your engine could also highlight “viewed this bought that” products.

Similar to ‘recently viewed’, this type of recommendation takes into account what products your site visitors have been engaging with.

However, this product recommendation engine takes it up a notch by also recommending other items your customers might like – smart cross-selling and upselling.

These recommendations obviously require some product data — specifically relating to the purchasing habits of other customers.

Another category of personalized recommendations is often referred to as “bought this bought that”.

Showing customers items that similar customers have bought is a great way to cross-sell and generate more sales by recommending complementary products.

This is also quite effective when sending these product recommendations in post-purchase emails, especially in your order confirmation emails.

Your customer might not purchase the recommended complementary product immediately, but they’ll still feel like it’s important to eventually purchase the item to get the most out of their latest purchase.

Frequently Bought Together

To pull off personalized product recommendation engines well, you require sufficient insight on your customers and their needs.

While this requires the most data, such as the customer’s activity, engagement and purchase history, it’s well worth the effort.

Targeted upselling and cross-selling, rather than recommendations based on generic categories of best sellers or new products, is much more relevant to your subscribers, and this will be reflected in your conversation rate.

A quick search online reveals tons of competitors in the product recommendation space.

Supply demand economics at work.

What is clear, however, is that online stores have witnessed a serious increase in conversion rates leveraging personalized recommendations. By way of example, one such tool is Tagalys, which stores use to display personalized recommendations. Tagalys generates real-time recommendations based on each visitor’s unique actions on your store.

The quality of the recommendations algorithms vary, and not all of them sync with your store to update products as your store changes.

However, using some recommendation engine, even if not perfect, is definitely better than doing nothing and becoming shark food.

As discussed above, it is critical to leverage your customers’ attention once you are finally able to get it.

Product recommendation engines provide a tremendous amount of value, and seriously improve your bottom line.

Of note, some forward looking stores are relying on chatbots to up their product recommendation game.  For example, check out Levis’ Ask Indigo bot.

Levi's Chatbot

Levi's Chatbot

Chatbots are more effective at upselling, cross-selling and generally recommending products because they can leverage their interaction with the customer to personalize the recommendation.

By way of an example, take a look at this Gobot chatbot leveraging its deep integration with Shopify to make effective product recommendations.

In this scenario the chatbot appears on your store after the bot notices that your customer is viewing a particular pair of jeans.

Gobot eCommerce Chatbot

Gobot eCommerce Chatbot

Gobot eCommerce Chatbot

Gobot leverages the power of your Shopify store and brings it to your chatbot!

As detailed in this Gobot help article, products in your store are kept in sync with Gobot so your bot will automatically update when your inventory or product selection changes.

In the above example, you would need to create an upselling category in your Shopify store of curated products that you wanted to offer to customers viewing the black Levis jeans sold on your store – and the chatbot does the rest.

The bot will appear 24/7, automatically, each and every time a visitor checks out the product page for your Levis jeans offering, and the chatbot will make sure that your visitor is aware of all of your curated complimentary offerings.

The bot will then navigate your visitors directly to the product page for any of the offerings clicked in the chatbot carousel so your customer can purchase directly through your Shopify store.

This chatbot can also automatically send follow-up emails with whatever content you might want to share, including but not limited to, product recommendations!

Gobot will also track all purchases resulting from interactions with your bot so you get a sense as to how much Gobot is increasing your bottom line!

Here’s another example chatbot from Gobot.

In this bot the store visitor is asked whether she wants assistance shopping and whether she is shopping for a male or female.

Gobot eCommerce Chatbot

Gobot eCommerce Chatbot

Gobot eCommerce Chatbot

Business owners have been trying to predict and control customer flow direction forever.  

The most familiar case is the supermarket, where shoppers are strategically guided through the store, and last minute temptations, or impulse purchases, are presented at the cash register.  

Most customers are right-handed and are said to be drawn to the right hand side of any space.  

Therefore, customers typically turn right in shops…or so they say – this is probably one of the most widely repeated claims.  

An alternative theory suggests that people navigate shops based on the side of the road they drive on (i.e. people go left in the UK and go right in the USA).

 Customer traffic flow has been studied in a wide variety of retail industries and in countries across the world.

In fact, there are factors which affect any “propensity” to turn in a particular direction.  

Understanding these factors and their influence on customer behavior help anticipate the direction of footfall and produce effective store layout plans that maximize retail performance.

So where does this leave our ability to predict or understand online shopper behaviour?

Do right handers focus more on the right side of the screen?

There certainly is an art to designing your website, and smart businesses leverage these natural human predilections to facilitate customer flow direction.  

But trying to influence “footfall” or eyeball direction is certainly not the most effective way of facilitating customer flow direction online, especially with the advent of chatbots!

Let’s bring Facilitating Customer Flow Direction into the 21st century, shall we?

Consider this scenario: Life is good, your online business is booming and you just bought a McMansion.  

Everything is furnished except the living room.

The living room is awesome.

It has a huge fireplace, dramatic arched beams, and wide plank oak floors.  

You’re committed to buying everything you need for this room online.

Where to start?

You check out some furniture websites.

They are pretty well organized, e.g., have tabs for dining tables, sofas, recliners, etc., and are seemingly very useful for someone who knows exactly what he or she wants – but that is not you.

 You’ve never furnished a room before and interior decorating has never been your strong suit.

Just as you are about to give up, and call an interior designer, your chatbot appears!

Gobot:

Welcome shopper!

Are you by chance looking to furnish a particular room?

  1. a)         Living room
  2. b)         Bedroom
  3. c)         Dining room

Your curiosity is piqued, and you click a).

Gobot:

When furnishing an entire room, some shoppers prefer to be shown curated collections rather than search for individual items.

Shall I show you some living rooms we have curated?

  1. a)         Yes
  2. b)         No

You click on Yes and you are brought to a rather rustic looking living room.  

The arrangement is nice, but just doesn’t do it for you.

Gobot:

What do you think?

  1. a)        More
  2. b)         I was considering something more conventional
  3. c)         Yes, I would like to explore this arrangement

If you click on a) or b) you would be shown different arrangements.  

However, you sort of like rustic (especially with your wide plank floors) and so you click c).

Gobot:

Glad you like it!

Which element in this selection is key for you?

  1. a)         Rug
  2. b)         Sofa
  3. c)         Table

The rug really caught your eye so you click a).

Gobot:

Excellent!

That’s our best selling rug, a very eye-catching design.

I can add that rug to your cart, yes?

You are presented with a call to action “Yes” button, which you click.

Gobot:

Ok, with the rug as your foundation allow me to present our curated complementary elements.

  1. a)         See complementary sofas?
  2. b)         See complementary coffee and side tables?
  3. c)         See complementary chairs?

You click on a).

You like the original sofa presented but it was a bit pricey and you want to explore other sofa options that match this room.

You are presented with different sofa options and choose a warm grey cushy style.

Gobot:

Nice, that’s our most comfortable sofa!

Care for matching pillows?

  1. a)         Yes
  2. b)         No thanks

You click on b) because you already have some perfectly matching pillows.

Gobot:

Now, we have the perfect chair that looks great with that sofa and rug, wanna see?

  1. a)         Yes
  2. b)         No thanks

You see how the story flows here…you fill up your cart and leave with an amazing living room perfectly curated for your home!

Chatbots help guide your direction through this virtual furniture store and utilize automatic, personalized cross-selling and upselling.  

Ikea tries to achieve this effect by literally guiding customers along an arrowed path through its enormous store.  

Pretty effective, if you have a couple of hours to spare!

The path weaves in and out of different rooms furnished with Ikea products.  

Chatbots can achieve an even better effect without a couple hundred thousand square feet of showroom!

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